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Immunotherapy deals with therapeutic interventions to arrest the progression of tumours using the immune system. These include checkpoint inhibitors, T-cell manipulation, cytokines, oncolytic viruses and tumour vaccines. In this paper, we present a survey of the latest developments on immunotherapy in colorectal cancer (CRC) and the role of artificial intelligence (AI) in this context. Among these, microsatellite instability (MSI) is perhaps the most popular IO biomarker globally. We first discuss the MSI status of tumours, its implications for patient management, and its relationship to immune response. In recent years, several aspiring studies have used AI to predict the MSI status of patients from digital whole-slide images (WSIs) of routine diagnostic slides. We present a survey of AI literature on the prediction of MSI and tumour mutation burden from digitised WSIs of haematoxylin and eosin-stained diagnostic slides. We discuss AI approaches in detail and elaborate their contributions, limitations and key takeaways to drive future research. We further expand this survey to other IO-related biomarkers like immune cell infiltrates and alternate data modalities like immunohistochemistry and gene expression. Finally, we underline possible future directions in immunotherapy for CRC and promise of AI to accelerate this exploration for patient benefits.

Background: Stress in medical students is a global concern. Stress disturbs the sympathovagal balance and affects heart rate variability, a sign of healthy cardiovascular homeostasis. Various strategies have been used globally to combat stress and its effects. Aim: To determine how blowing balloon exercise modulatesheart rate variability (HRV) in stressed medical students. Methodology: A pretest-posttest quasi-experimental study wasconducted among stressed medical students at Islamic international medical college Rawalpindi Pakistan. Sixty students diagnosed with moderate stress were enrolled based on Depression Anxiety Stress Scale (DASS) Proforma. The study population was selected by simple random sampling via the balloting method among the stressed medical students. Study participants underwent a supervised blowing balloon exercise.Before and after a supervised blowing balloon exercise, the participants' pulmonary function tests, electrocardiograms, and DASS scores were measured. Participants' ECGs and pulmonary function tests were recorded viaPower lab (A.D. Instruments, model yam 4/25T, South Wales, Australia). Results: Blowing balloon exercise resulted in a significant reduction of DASS score (p

SummaryIdentification of gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from (WSIs) using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinically and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining significant biological insights from routine pathology imaging directly.Graphical AbstractHighlightsData-driven discovery of co-expressing gene groups in breast canerHistological imaging based prediction of gene groups via deep learningIdentification of phenotypic correlates of gene-expression in histological imagingClinical and therapeutic impact of gene groups and their visual patterns identified

Glycation, oxidation, nitration, and crosslinking of proteins are implicated in the pathogenic mechanisms of type 2 diabetes, cardiovascular disease, and chronic kidney disease. Related modified amino acids formed by proteolysis are excreted in urine. We quantified urinary levels of these metabolites and branched-chain amino acids (BCAAs) in healthy subjects and assessed changes in early-stage decline in metabolic, vascular, and renal health and explored their diagnostic utility for a noninvasive health screen. We recruited 200 human subjects with early-stage health decline and healthy controls. Urinary amino acid metabolites were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning was applied to optimise and validate algorithms to discriminate between study groups for potential diagnostic utility. Urinary analyte changes were as follows: impaired metabolic health—increased Nε-carboxymethyl-lysine, glucosepane, glutamic semialdehyde, and pyrraline; impaired vascular health—increased glucosepane; and impaired renal health—increased BCAAs and decreased Nε-(γ-glutamyl)lysine. Algorithms combining subject age, BMI, and BCAAs discriminated between healthy controls and impaired metabolic, vascular, and renal health study groups with accuracy of 84%, 72%, and 90%, respectively. In 2-step analysis, algorithms combining subject age, BMI, and urinary Nε-fructosyl-lysine and valine discriminated between healthy controls and impaired health (any type), accuracy of 78%, and then between types of health impairment with accuracy of 69%-78% (cf. random selection 33%). From likelihood ratios, this provided small, moderate, and conclusive evidence of early-stage cardiovascular, metabolic, and renal disease with diagnostic odds ratios of 6 – 7, 26 – 28, and 34 – 79, respectively. We conclude that measurement of urinary glycated, oxidized, crosslinked, and branched-chain amino acids provides the basis for a noninvasive health screen for early-stage health decline in metabolic, vascular, and renal health. Other information Published in: Oxidative Medicine and Cellular Longevity License: http://creativecommons.org/licenses/by/4.0 See article on publisher's website: http://dx.doi.org/10.1155/2019/4851323

BackgroundHistopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. With the aims of facilitating diagnostic decision-making and improving the use of pathologists’ time, we developed an AI-based pre-screening tool that analyses whole slide images (WSIs) of large bowel biopsies to identify normal, inflammatory, and neoplastic biopsies.MethodsTo learn the differential histological patterns from digitised WSIs of large bowel biopsy slides stained with Haematoxylin and Eosin (H&E), our proposed weakly supervised deep learning method uses only slide-level diagnostic labels and no detailed cell or region-level annotations. The proposed method was developed on an internal cohort of biopsy slides (n=5054) from a single laboratory labelled with corresponding diagnostic categories assigned by pathologists. Performance of the tool was evaluated on the internal development cohort (n=5054) in a cross-validation setting, and three external unseen cohorts (n=1536) for independent validation.FindingsThe proposed tool demonstrates high degree of accuracy to assist with the pre-screening of large bowel biopsies, being able to identify neoplastic biopsies (AUROC = 0·993), inflammatory biopsies (AUROC = 0·966) and all abnormal biopsies (AUROC = 0·979). On the three independent validation cohorts, it achieves AUROC values of 0·943, 0·958 and 0·964 for the detection of abnormal biopsies. Analysis of saliency maps confirms the representation of disease heterogeneity in model predictions and their association with relevant histological features. Interestingly, after examining diagnostic discrepancies between the proposed AI tool and original diagnostic labels, a panel of pathologists found that the proposed tool correctly identified a number of abnormal slides that had been initially reported as normal.InterpretationsThe proposed tool with its high sensitivity of detecting abnormal colorectal biopsies promises significant improvements in clinical workflow efficiency and assistance in diagnostic decision-making through pre-screening of normal biopsies.FundingInnovate UK on behalf of UK Research and Innovation.

Salivary gland tumors (SGT) are aheterogeneous neoplasms with large morphological diversity and overlapping features. Recently, numerous artificial intelligence (AI) methods shown for reproducible histological diagnosis and prognosis. However, their application to SGT has not been reported to date. This study aims to examine if AI can be used to differentiate between different SGT subtypes based on the analysis of digitized whole-slide images (WSIs) of Haematoxylin and Eosin (H&E) stained slides. A two-stage machine learning (ML) algorithm was developed and tested on 240 scanned H&E WSIs of SGT cases using an open-source bioimage analysis software (QuPath) to train and analyze features on representative regions of interest. The first classifier was designed to differentiate between two benign and four malignant SGT subtypes with an equal split between benign and malignant SGTs (n = 120 each), while the second classifier was used for malignant SGT subtyping (n = 120). Features extracted using the ML classifiers were also analysed using deep learning (DL) networks to determine any performance improvements. Our first classifier showed excellent accuracy for automated differentiation between benign and malignant SGTs (F1-score = 0.90). The second classifier also performed well for differentiation between four different malignant SGTs (average F1 = 0.92). Significant differences between cellularity, nuclear hematoxylin, cytoplasmic eosin, and nucleus/cell ratio (p 0.80), with EfficientNet-B0 giving the best performance (F1 = 0.87) but with inferior accuracy than the ML classifier for malignant subtyping (highest F1 = 0.60 for ResNet-18 and ResNet-50). Our novel findings show that AI can be used for automated differentiation between benign and malignant SGT and tumor subtyping on H&E images. Analysis of a larger multicentre cohort using ML and DL at the WSI level is required to establish the significance and clinical usefulness of these findings.

ObjectiveTo develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis.DesignA graph neural network was developed incorporating pathologist domain knowledge to classify 6591 whole-slides images (WSIs) of endoscopic large bowel biopsies from 3291 patients (approximately 54% female, 46% male) as normal or abnormal (non-neoplastic and neoplastic) using clinically driven interpretable features. One UK National Health Service (NHS) site was used for model training and internal validation. External validation was conducted on data from two other NHS sites and one Portuguese site.ResultsModel training and internal validation were performed on 5054 WSIs of 2080 patients resulting in an area under the curve-receiver operating characteristic (AUC-ROC) of 0.98 (SD=0.004) and AUC-precision-recall (PR) of 0.98 (SD=0.003). The performance of the model, named Interpretable Gland-Graphs using a Neural Aggregator (IGUANA), was consistent in testing over 1537 WSIs of 1211 patients from three independent external datasets with mean AUC-ROC=0.97 (SD=0.007) and AUC-PR=0.97 (SD=0.005). At a high sensitivity threshold of 99%, the proposed model can reduce the number of normal slides to be reviewed by a pathologist by approximately 55%. IGUANA also provides an explainable output highlighting potential abnormalities in a WSI in the form of a heatmap as well as numerical values associating the model prediction with various histological features.ConclusionThe model achieved consistently high accuracy showing its potential in optimising increasingly scarce pathologist resources. Explainable predictions can guide pathologists in their diagnostic decision-making and help boost their confidence in the algorithm, paving the way for its future clinical adoption.

Diagnostic, prognostic and therapeutic decision-making of cancer in pathology clinics can now be carried out based on analysis of multi-gigapixel tissue images, also known as whole-slide images (WSIs). Recently, deep convolutional neural networks (CNNs) have been proposed to derive unsupervised WSI representations; these are attractive as they rely less on expert annotation which is cumbersome. However, a major trade-off is that higher predictive power generally comes at the cost of interpretability, posing a challenge to their clinical use where transparency in decision-making is generally expected. To address this challenge, we present a handcrafted framework based on deep CNN for constructing holistic WSI-level representations. Building on recent findings about the internal working of the Transformer in the domain of natural language processing, we break down its processes and handcraft them into a more transparent framework that we term as the Handcrafted Histological Transformer or H2T. Based on our experiments involving various datasets consisting of a total of 5,306 WSIs, the results demonstrate that H2T based holistic WSI-level representations offer competitive performance compared to recent state-of-the-art methods and can be readily utilized for various downstream analysis tasks. Finally, our results demonstrate that the H2T framework can be up to 14 times faster than the Transformer models.

Cross-slide image analysis provides additional information by analysing the expression of different biomarkers as compared to a single slide analysis. These biomarker stained slides are analysed side by side, revealing unknown relations between them. During the slide preparation, a tissue section may be placed at an arbitrary orientation as compared to other sections of the same tissue block. The problem is compounded by the fact that tissue contents are likely to change from one section to the next and there may be unique artefacts on some of the slides. This makes registration of each section to a reference section of the same tissue block an important pre-requisite task before any cross-slide analysis. We propose a deep feature based registration (DFBR) method which utilises data-driven features to estimate the rigid transformation. We adopted a multi-stage strategy for improving the quality of registration. We also developed a visualisation tool to view registered pairs of WSIs at different magnifications. With the help of this tool, one can apply a transformation on the fly without the need to generate transformed source WSI in a pyramidal form. We compared the performance of data-driven features with that of hand-crafted features on the COMET dataset. Our approach can align the images with low registration errors. Generally, the success of non-rigid registration is dependent on the quality of rigid registration. To evaluate the efficacy of the DFBR method, the first two steps of the ANHIR winner's framework are replaced with our DFBR to register challenge provided image pairs. The modified framework produces comparable results to that of challenge winning team.

Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma (OSCC). Histological grading is the current gold standard for OED prognostication but is subjective and variable with unreliable outcome prediction. We explore if individual OED histological features can be used to develop and evaluate prognostic models for malignant transformation and recurrence prediction. Digitised tissue slides for a cohort of 109 OED cases were reviewed by three expert pathologists, where the prevalence and agreement of architectural and cytological histological features was assessed and association with clinical outcomes analysed using Cox proportional hazards regression and Kaplan-Meier curves. Within the cohort, the most prevalent features were basal cell hyperplasia (72%) and irregular surface keratin (60%), and least common were verrucous surface (26%), loss of epithelial cohesion (30%), lymphocytic band and dyskeratosis (34%). Several features were significant for transformation (p

Histopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. To facilitate the diagnostic decision-making and reduce the workload of pathologists, we present an AI-based pre-screening tool capable of identifying normal and neoplastic colon biopsies. To learn the differential histological patterns from whole slides images (WSIs) stained with hematoxylin and eosin (H&E), our proposed weakly supervised deep learning method requires only slide-level labels and no detailed cell or region-level annotations. The proposed method was developed and validated on an internal cohort of biopsy slides (n=4 292) from two hospitals labeled with corresponding diagnostic categories assigned by pathologists after reviewing case reports. Performance of the proposed colon cancer pre-screening tool was evaluated in a cross-validation setting using the internal cohort (n=4 292) and also by an external validation on The Cancer Genome Atlas (TCGA) cohort (n=731). With overall cross-validated classification accuracy (AUROC = 0.9895) and external validation accuracy (AUROC = 0.9746), the proposed tool promises high accuracy to assist with the pre-screening of colorectal biopsies in clinical practice. Analysis of saliency maps confirms the representation of disease heterogeneity in model predictions and their association with relevant pathological features. The proposed AI tool correctly reported some slides as neoplastic while clinical reports suggested they were normal. Additionally, we analyzed genetic mutations and gene enrichment analysis of AI-generated neoplastic scores to gain further insight into the model predictions and explore the association between neoplastic histology and genetic heterogeneity through representative genes and signaling pathways.

Motivation Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows. However, the estimation of robustness of CPath models to variations in input images is an open problem with a significant impact on the downstream practical applicability, deployment and acceptability of these approaches. Furthermore, development of domain-specific strategies for enhancement of robustness of such models is of prime importance as well. Results In this work, we propose the first domain-specific Robustness Evaluation and Enhancement Toolbox (REET) for computational pathology applications. It provides a suite of algorithmic strategies for enabling robustness assessment of predictive models with respect to specialized image transformations such as staining, compression, focusing, blurring, changes in spatial resolution, brightness variations, geometric changes as well as pixel-level adversarial perturbations. Furthermore, REET also enables efficient and robust training of deep learning pipelines in computational pathology. Python implementation of REET is available at https://github.com/alexjfoote/reetoolbox. Supplementary information Supplementary data are available at Bioinformatics online.

Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process. This work explores the viability and timing comparisons of eye gaze labeling compared to conventional manual labeling for training object detectors. Challenges associated with gaze based labeling and methods to refine the coarse data annotations for subsequent object detection are also discussed. Results demonstrate that gaze tracking based labeling can save valuable pathologist time and delivers good performance when employed for training a deep object detector. Using the task of localization of Keratin Pearls in cases of oral squamous cell carcinoma as a test case, we compare the performance gap between deep object detectors trained using hand-labelled and gaze-labelled data. On average, compared to `Bounding-box' based hand-labeling, gaze-labeling required $57.6\%$ less time per label and compared to `Freehand' labeling, gaze-labeling required on average $85\%$ less time per label., Comment: 12 pages, 10 figures, 2 tables, journal

The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue and hence facilitate the extraction of interpretable features for biomarker discovery. However, these models are typically trained for a single task and therefore scale poorly as we wish to adapt the model for an increasing number of different tasks. Also, supervised deep learning models are very data hungry and therefore rely on large amounts of training data to perform well. In this paper, we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumina and different tissue regions that leverages data from multiple independent data sources. While ensuring that our tasks are aligned by the same tissue type and resolution, we enable meaningful simultaneous prediction with a single network. As a result of feature sharing, we also show that the learned representation can be used to improve the performance of additional tasks via transfer learning, including nuclear classification and signet ring cell detection. As part of this work, we train our developed Cerberus model on a huge amount of data, consisting of over 600 thousand objects for segmentation and 440 thousand patches for classification. We use our approach to process 599 colorectal whole-slide images from TCGA, where we localise 377 million, 900 thousand and 2.1 million nuclei, glands and lumina respectively. We make this resource available to remove a major barrier in the development of explainable models for computational pathology.

Human epidermal growth factor receptor 2 (HER2) is an important prognostic and predictive factor which is overexpressed in 15-20% of breast cancer (BCa). The determination of its status is a key clinical decision making step for selection of treatment regimen and prognostication. HER2 status is evaluated using transcriptomics or immunohistochemistry (IHC) through in-situ hybridisation (ISH) which incurs additional costs and tissue burden and is prone to analytical variabilities in terms of manual observational biases in scoring. In this study, we propose a novel graph neural network (GNN) based model (SlideGraph

Objectives: Type 2 diabetes is characterized by hyperglycemia and occurs as a result of insulin resistance and pancreatic beta cells failure to compensate. Present study was conducted to determine the antidiabetic effect of Withania coagulans and liraglutide on postprandial serum insulin levels and pancreatic histological features in streptozotocin induced diabetic rat. Study Design: This Randomized Control Trial. Setting: At multidisciplinary lab Islamic International Medical College, Rawalpindi in collaboration with Animal House, National Institute of Health, Islamabad. Period: From March 2016 to April 2017. Material and Methods: Forty male Sprague daily rats were randomly divided into normal Control Group A (n=10) and Experimental Group (n=30). Experimental group was given streptozotocin (30mg/kg/day) intraperitoneally for 5 days and diabetes was confirmed in experimental group by measuring fasting blood glucose level (mg/dl). Experimental group was divided further into Group B (Diabetic control), Group C (Withania coagulans treated) and Group D (Liraglutide treated). Group C were given Withania coagulans in addition to normal diet and Group D received Liraglutide besides normal diet for 30 days. Second sampling for evaluating postprandial serum insulin level and pancreatic histology was done after 30 days. Result: Postprandial serum insulin levels of Withania coagulans treated Group C (5.54 + 0.23 μU/ml) and Liraglutide treated Group D (6.06 + 0.17 μU/ml) were significantly raised in comparison with Diabetic control Group B (3.50 + 0.19 μU/ml), whereas islets of Langerhans cell morphology were markedly improved in Group C and D as compared to Group B. Conclusion: Withania coagulans significantly increases postprandial serum insulin level and improves pancreatic beta cells architecture.

Objective: To evaluate effect of Withania coagulans and liraglutide on serum Glucagon like peptide-1, Postprandial and Fasting Blood Glucose levels in streptozotocin induced diabetic rat. Study Design and Setting: This randomize control trile was conducted at Islamic International Medical College in collaboration with National Institute of Health Islamabad. Methodology: This randomized controlled study was performed on a total of forty male Sprague dawly rats, which were initially divided into two groups; Group A (n=10) and Experimental Group (n=30). Diabetes in the Experimental group B was induced by intraperitoneal administration of streptozotocin for 5 days (30mg/kg/day). Diabetes was checked in experimental group by measuring fasting blood glucose (mg/dl) on day 6. Experimental group was further divided into Group B (Diabetic control), Group C (Withania coagulans-treated) and Group D (Liraglutide-treated). Blood sampling was done at day 30 and serum GLP-1, postprandial and fasting blood glucose levels were measured and compared in all groups. Results: Fasting and postprandial blood glucose levels of group C and D were significantly reduced as compared to group B. Serum GLP-1 levels were significantly increased in group C and D as compared to group B. Conclusion: Withania coagulans reduces hyperglycemia in diabetic rats through increasing GLP-1 hormone.

Identification of nuclear components in the histology landscape is an important step towards developing computational pathology tools for the profiling of tumor micro-environment. Most existing methods for the identification of such components are limited in scope due to heterogeneous nature of the nuclei. Graph-based methods offer a natural way to formulate the nucleus classification problem to incorporate both appearance and geometric locations of the nuclei. The main challenge is to define models that can handle such an unstructured domain. Current approaches focus on learning better features and then employ well-known classifiers for identifying distinct nuclear phenotypes. In contrast, we propose a message passing network that is a fully learnable framework build on classical network flow formulation. Based on physical interaction of the nuclei, a nearest neighbor graph is constructed such that the nodes represent the nuclei centroids. For each edge and node, appearance and geometric features are computed which are then used for the construction of messages utilized for diffusing contextual information to the neighboring nodes. Such an algorithm can infer global information over an entire network and predict biologically meaningful nuclear communities. We show that learning such communities improves the performance of nucleus classification task in histology images. The proposed algorithm can be used as a component in existing state-of-the-art methods resulting in improved nucleus classification performance across four different publicly available datasets.

Automated synthesis of histology images has several potential applications including the development of data-efficient deep learning algorithms. In the field of computational pathology, where histology images are large in size and visual context is crucial, synthesis of large high-resolution images via generative modeling is an important but challenging task due to memory and computational constraints. To address this challenge, we propose a novel framework called SAFRON (Stitching Across the FROntier Network) to construct realistic, large high-resolution tissue images conditioned on input tissue component masks. The main novelty in the framework is integration of stitching in its loss function which enables generation of images of arbitrarily large sizes after training on relatively small image patches while preserving morphological features with minimal boundary artifacts. We have used the proposed framework for generating, to the best of our knowledge, the largest-sized synthetic histology images to date (up to 11K×8K pixels). Compared to existing approaches, our framework is efficient in terms of the memory required for training and computations needed for synthesizing large high-resolution images. The quality of generated images was assessed quantitatively using Frechet Inception Distance as well as by 7 trained pathologists, who assigned a realism score to a set of images generated by SAFRON. The average realism score across all pathologists for synthetic images was as high as that of real images. We also show that training with additional synthetic data generated by SAFRON can significantly boost prediction performance of gland segmentation and cancer detection algorithms in colorectal cancer histology images.

Recent advances in whole‐slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence‐based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilise information embedded in pathology WSIs beyond what can be obtained through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue, and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms that are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well‐defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large‐scale annotation exercise involving a multidisciplinary team of pathologists, ML experts, and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real‐world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary, and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.

4576 Background: Bladder Urothelial Carcinoma (BLCA) is the most common type of bladder cancer and the sixth most frequent cancer in the US. High numbers of lymphocytes colocated with tumour-associated stromal (TAS) tissue has demonstrated prognostic significance for overall survival in multiple cancers. Our goal in this study was to explore the prognostic significance of an automated score to quantify the colocalisation of lymphocytes and TAS for overall survival (OS) in BLCA patients from Haematoxylin & Eosin (H&E) stained Whole Slide Images (WSIs). Methods: Two cohorts of BLCA patients were included in this study. From the UK, a cohort of 67 BLCA patients constituted cohort A. We also evaluated our method on The Cancer Genome Atlas (TCGA) bladder cancer cohort of 453 cases, which we refer to as cohort B. We developed a two-stage method for digital quantification of lymphocytic infiltrates in TAS. First, we employed an AI algorithm to recognise and classify different regions as areas of high concentration of tumour, lymphocytes and stroma for each WSI creating a segmentation map of the different tissue types. For patients with multiple WSIs, we used the slide with the highest percentage of tumour to predict survival. This algorithm had been pre-trained on a cohort of Oral cancer and was fine-tuned using annotations from 4 BLCA WSIs, 3 from cohort A and 1 from cohort B. These WSIs were excluded from the final survival analysis. Using the segmentation maps, a statistical measure for the colocalisation of TAS and lymphocytes termed the tumour-associated stroma infiltrating lymphocytes or (TASIL) score was computed. Finally, for each cohort, data was right-censored at 10 years and the digital BLCA-TASIL (BT) score’s prognostic significance for OS was investigated by fitting a Kaplan-Meier estimator and Cox proportional hazard (PH) analysis, stratifying patients into two groups based on the BT score. In each cohort, two thirds of the data was used as the discovery set to determine the best cut-off for the TASIL Score and the remaining third was used as the validation set. Results: Our classification algorithm achieved high average F1-score of 0.88 on a held-out set of unseen data for the classification of tumour, lymphocytic and stromal regions. In cohort A, higher BT score was strongly associated with better OS ( P= 0.00906) on the unseen validation data. This significant association was also found in the validation data of cohort B ( P< 0.001). Using the BT score as the only covariate, a Cox PH model for the validation data resulted in a C-index of 0.73 for cohort A and 0.57 for cohort B, respectively. Conclusions: The digital BT score showed significant prognostic value for overall survival in both BLCA cohorts, reinforcing the findings of prior work. We intend to further validate these findings on another cohort. To the best of our knowledge, this is the first attempt to predict survival solely from H&E slides in BLCA.

Background: The factors associated with infection in oral candida are countless. This infection is mainly caused by bad prosthetic cleanliness. Objective: The goal of current research study was to assess that how oral candida growth is influenced by wearing false teeth all over night and gender. Patients and Methods: The present research was organized at Sir Ganga Ram Hospital, Lahore (October 2017 to August 2019). Total patients enrolled for this research were forty. The males and females were in equal number i.e. 20. These patients were between fifty to sixty-five years of age and complete denture wearer for first time. Those patients were not selected for the study who were found with smoking habit, diabetes and with the record of treatment with radiotherapy and chemotherapy in the previous six months. For the assemblage of sample, the method used was oral rinse on some time in a day. In this method, each patient was given ten milliliter of sterile saline in a sterile container that was disposable. They were asked to rinse for the time period of sixty seconds. Quantitative and qualitative growth of candida was examined and Gram stain, microscopy and colony count were measured after sending the sample to Microbiology section of pathology department of the hospital. Results: It is concluded by the results that candida growth is increased by overnight denture wearing. It was also observed that after one month of wearing complete denture, the dissimilarity in candida growth in females was valuable. Conclusion: It is concluded that dentures should be removed during night, as in case of eyeglasses. In this way, denture stomatitis will be avoided. Moreover, information should be provided about oral cleanliness to common people, especially to females. Keywords: Denture, Candida and Sterile Container.

Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localized scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Regions of tumor in a series of fifty consecutive breast core biopsies were identified by annotation on HE whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883, respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration. © 2016 International Society for Advancement of Cytometry.

The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise.